Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D pose of a few people at high resolution. Furthermore, many applications such as autonomous driving or crowd analysis require pose estimation of a large number of people possibly at low-resolution. In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based Network), a new single-shot, anchor-based and multi-person 3D pose estimation approach. The proposed model performs bounding box detection and, for each detected person, 2D and 3D pose regression into a single forward pass. It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box and allows the pose estimation of a possibly large number of people at low resolution. To manage people overlapping, we introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists between different people sizes in the image, and joints coordinates have different uncertainties depending on these sizes, we propose a method to automatically optimize weights associated to different people scales and joints for efficient training. PandaNet surpasses previous single-shot methods on several challenging datasets: a multi-person urban virtual but very realistic dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU Panoptic and MuPoTS-3D).

This paper presents two methods for building lightweight neural networks with similar accuracy than heavyweight ones with the advantage to be less greedy in memory and computing resources. So it can be implemented in edge and IoT devices. The presented distillation methods are respectively based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The two methods basically rely on the successive dimension reduction of a given dense neural network (teacher) hidden features, and the learning of a smaller neural network (student) which solves the initial learning problem along with a mapping problem to the reduced successive features spaces. The presented methods are compared to baselines -learning the student networks from scratch-, and we show that the additional mapping problem significantly improves the performance (accuracy, memory and computing resources) of the student networks.

In this paper, we propose a new single shot method for multi-person 3D pose estimation, from monocular RGB images. Our model jointly learns to locate the human joints in the image, to estimate their 3D coordinates and to group these predictions into full human skeletons. Our approach leverages and extends the Stacked Hourglass Network and its multi-scale feature learning to manage multi-person situations. Thus, we exploit the Occlusions Robust Pose Maps (ORPM) to fully describe several 3D human poses even in case of strong occlusions or cropping. Then, joint grouping and human pose estimation for an arbitrary number of people are performed using associative embedding. We evaluate our method on the challenging CMU Panoptic dataset, and demonstrate that it achieves better results than the state of the art.